Collaborative Research: Urban Vector-Borne Disease Transmission Demands Advances in Spatiotemporal Statistical Inference

统计分析部分观测，非线性，随机时空系统是一个方法上的挑战。许多现有的推理算法都受到“维度诅咒”的困扰，这使得它们无法适用于描述发生在许多空间位置内部和之间的相互作用动态过程的模型。将开发新的算法，并在理论和实践中展示，以提高时空数据分析的能力。这项方法学研究将在处理登革热病毒传播这一公共卫生问题的背景下进行。在过去50年里，登革热的全球发病率上升了30倍，南美洲和中美洲的地理范围明显扩大。里约热内卢市是该地区登革热传播的一个焦点。将分析巴西巴西登革热病例的时空数据，以及人类活动、温度和降雨数据。通过对疾病传播的基于模型的理解，可以为检测、控制和可能根除传染病的政策决定提供信息。提高对疾病传播时空动态的理解将对疾病控制的改进产生影响。将开发数学模型来描述登革热传播的时空动态，并将使用新的统计方法将这些模型与巴西巴西的数据联系起来。时空部分观测马尔可夫过程模型提供了一个框架，用于制定和回答与潜在随机动态过程相关的时空数据问题。统计上有效的推理包括对潜在过程的可能值进行积分，这是一项被称为过滤的任务。除了当系统近似线性和高斯时，过滤时空模型是具有挑战性的。在这个项目中开发的一种算法将通过引导蒙特卡罗粒子走向潜在变量空间的重要区域来解决维度的诅咒。另一种算法将结合许多弱的、独立的滤波器来给出一个全局滤波解决方案。疾病传播系统是高度非线性和随机的，并且是不完全可观察的，将用于激励和展示新算法的能力。具体而言，将为里约热内卢主要大都市的登革热传播动态开发模型。时空随机流行病学模型将用于研究人类流动性、宿主免疫和气候变异在异质社会经济景观背景下的作用。一个特别的目标是确定对疾病侵袭和持续存在至关重要的感染源以及无法持续在当地传播的汇的位置。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Statistical analysis of partially-observed, nonlinear, stochastic spatiotemporal systems is a methodological challenge. Many existing inference algorithms suffer from a "curse of dimensionality" that prohibits their applicability to models describing interacting dynamic processes occurring within and between many spatial locations. New algorithms will be developed, and shown in theory and in practice to advance capabilities for spatiotemporal data analysis. This methodological research will be carried out in the context of addressing a public health concern, transmission of dengue virus. Global incidence of dengue has risen 30-fold over the past fifty years, with notable geographical expansion in South and Central America. The municipality of Rio de Janeiro is a focal point for dengue transmission in this region. Spatiotemporal data on dengue cases in Rio de Janeiro will be analyzed, together with data on human movement, temperature, and rainfall. Policy decisions for the detection, control, and potential eradication of infectious diseases are informed by model-based understanding of disease transmission. Improved understanding of the spatiotemporal dynamics of disease transmission will have implications for improvements in disease control. Mathematical models will be developed to describe spatiotemporal dynamics of dengue transmission, and the novel statistical methodology will be used to link these models to the data from Rio de Janeiro.Spatiotemporal partially-observed Markov process models provide a framework for formulating and answering questions relating spatiotemporal data to an underlying stochastic dynamic process. Statistically efficient inference involves integrating out over possible values of the latent process, a task known as filtering. Except when the system is approximately linear and Gaussian, filtering spatiotemporal models is challenging. One algorithm developed in this project will address the curse of dimensionality by guiding Monte Carlo particles toward important regions in the latent variable space. Another algorithm will combine many weak, independent filters to give a global filtering solution. Disease transmission systems, which are highly nonlinear and stochastic and are imperfectly observable, will be used to motivate and demonstrate the capabilities of the new algorithms. Specifically, models will be developed for the dynamics of dengue transmission in the major metropolis of Rio de Janeiro. Spatiotemporal stochastic epidemiological models will be used to examine the role of human mobility, host immunity, and climate variability in the context of a heterogeneous socioeconomic landscape. A particular goal is to identify locations that function as sources of infection critical to disease invasion and persistence as well as those that act as sinks incapable of sustained local transmission.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.